Emerging Diagnostic Glucose Sensing Approaches

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  • ISBN: 9780443363481
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Emerging Diagnostic Glucose Sensing Approaches: Diabetes

is a comprehensive review of past and recent research highlighting benefits of techniques to analyze the methods and technologies being employed with the aim to perform successful pain-free blood glucose monitoring using non-invasive techniques.

The book's main focus is given on the use of the electromagnetic waves to analyze the physical phenomena while using advanced sensors and physiological techniques for glucose monitoring in the human body.

Measuring the blood glucose levels using electromagnetic waves is now one of the most prominent candidates fulfilling the criteria for blood glucose monitoring.

The second is the research using Physiological techniques (ECG and EEG) integrating artificial intelligence/deep learning.

Success in this research requires the applications of physical models for accurate analytical studies, the use of biomedical methodologies for understanding the human body phenomena, and the application of engineering tools for the development of the prototypes.

This is a must-have reference of recent breakthroughs and cutting-edge techniques using advanced electromagnetic wave sensors, which have opened up new areas in the non-invasive blood glucose research for the biomedical and other research communities.

1. Diabetes, Glucose sensing, Requirements, Challenges and Techniques

2. Current Progress in RF sensing for Glucose monitoring

3. Electromagnetics wave Sensors: Design, Fabrication, and Development

4. Testing methods: Invitro and Invivo

5. Physiological Technique: Method, Need and Advantages

6. Prediction of prediabetes: Artificial neural networking

7. Spatiotemporal ECG and EEG feature analysis

8. Bioimpedance based approach

9. Convolutional neural networking: Mult segments fusion and Varied weight

10. Portable and noninvasive blood glucose monitoring

11. Role of Surface waves and Goubau line

12. Statistical and spectral analysis of ECG signal

13. Deep learning intervention for health care challenges

14. Sensing and control: Future and Vision

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